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Artificial Computational Intelligence in Generating Generalized Profile Function Model

机译：生成广义剖面函数模型中的人工计算智能

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In this paper a derivation of a generalized profile function model, GPFM, based on artificial intelligence is described. This generalized model provides an approximation of the profile function of any object in region. The procedure based on artificial computational intelligence, that is, neural networks, is very efficient and gives very good results. The GPFM, is generated using the basic dataset and verification is performed by the validation data set. The summary statistical parameters of the original, measured data and estimated data, based on the GPFM, are presented and compared. The test of the obtained GPFM, is also performed by regression analysis. The obtained correlation coefficients between the real, measured data and estimated data are very high, 0.9946 for the basic and 0.9933 for the validation dataset.
机译：在本文中，描述了一种基于人工智能的广义简档功能模型GPFM的推导。该广义模型提供了区域中任何对象的轮廓函数的近似值。基于人工计算智能的程序，即神经网络，非常有效，提供了非常好的结果。使用基本数据集生成GPFM并由验证数据集执行验证。提出并比较了原始，测量数据和估计数据的摘要统计参数，并进行了比较。所获得的GPFM的测试也通过回归分析进行。实验数据和估计数据之间获得的相关系数非常高，为基本数据集0.9946，对于验证数据集是0.9933。

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